{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年1月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Salary_Mean</th>\n",
       "      <th>Salary_Median</th>\n",
       "      <th>JD_Count</th>\n",
       "      <th>HeadCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>201906</td>\n",
       "      <td>12990</td>\n",
       "      <td>12000</td>\n",
       "      <td>98669</td>\n",
       "      <td>323172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>201907</td>\n",
       "      <td>13209</td>\n",
       "      <td>12000</td>\n",
       "      <td>94918</td>\n",
       "      <td>308802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>201908</td>\n",
       "      <td>13388</td>\n",
       "      <td>12500</td>\n",
       "      <td>94569</td>\n",
       "      <td>303830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>201909</td>\n",
       "      <td>13421</td>\n",
       "      <td>12500</td>\n",
       "      <td>90804</td>\n",
       "      <td>292998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>201910</td>\n",
       "      <td>13337</td>\n",
       "      <td>12500</td>\n",
       "      <td>87620</td>\n",
       "      <td>284579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>201911</td>\n",
       "      <td>13451</td>\n",
       "      <td>12500</td>\n",
       "      <td>84685</td>\n",
       "      <td>277672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>201912</td>\n",
       "      <td>13554</td>\n",
       "      <td>12500</td>\n",
       "      <td>84141</td>\n",
       "      <td>276906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>202001</td>\n",
       "      <td>13632</td>\n",
       "      <td>12500</td>\n",
       "      <td>78009</td>\n",
       "      <td>258456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month  Salary_Mean  Salary_Median  JD_Count  HeadCount\n",
       "0  201906        12990          12000     98669     323172\n",
       "1  201907        13209          12000     94918     308802\n",
       "2  201908        13388          12500     94569     303830\n",
       "3  201909        13421          12500     90804     292998\n",
       "4  201910        13337          12500     87620     284579\n",
       "5  201911        13451          12500     84685     277672\n",
       "6  201912        13554          12500     84141     276906\n",
       "7  202001        13632          12500     78009     258456"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_data = pd.read_sql(sql='select * from MonthlyStats order by Month', con=conn)\n",
    "stat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\"Month\",\"Salary_Mean\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=[]\n",
    "percentages=[]\n",
    "count1=conn.execute(f\"select COUNT(1) from _{year}{month:02}\").fetchall()[0][0]\n",
    "counts.append(count1)\n",
    "\n",
    "for i in range(1,month-6+1):\n",
    "    i_count=conn.execute(f\"select COUNT(1) from _{year}{month:02} a inner join _{year}{month-i:02} b on a.job_id=b.job_id\").fetchall()[0][0]\n",
    "    counts.append(i_count)\n",
    "    percentages.append((counts[i-1]-i_count)/counts[0])\n",
    "    \n",
    "percentages.append(counts[-1]/counts[0])\n",
    "print(percentages)\n",
    "\n",
    "labels=[]\n",
    "for i in range(1,month-6+1):\n",
    "    labels.append(i)\n",
    "labels.append(f\"{str(month-6+1)}+\")\n",
    "\n",
    "plt.pie(percentages, labels=labels)\n",
    "plt.title(\"Age of Jobs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照职能统计平均工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "区块链工程师 平均工资为21119\n"
     ]
    }
   ],
   "source": [
    "sql=f'select AVG(monthly_salary)  from _{year}{month:02} where expert_blockchain=1 and monthly_salary>0'\n",
    "salary_blockchain=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'区块链工程师 平均工资为{salary_blockchain}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "算法工程师 平均工资为19317\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month:02} where monthly_salary>0 and career='算法工程师'\"\n",
    "salary_algorithm=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'算法工程师 平均工资为{salary_algorithm}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "系统架构师 平均工资为23244\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month:02} where monthly_salary>0 and career='系统架构师'\"\n",
    "salary_architect=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'系统架构师 平均工资为{salary_architect}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Java工程师 平均工资为14227\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month:02} where monthly_salary>0 and pl_java=1\"\n",
    "salary_java=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'Java工程师 平均工资为{salary_java}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python工程师 平均工资为18072\n"
     ]
    }
   ],
   "source": [
    "sql=f\"select AVG(monthly_salary)  from _{year}{month:02} where monthly_salary>0 and pl_python=1\"\n",
    "salary_python=conn.execute(sql).fetchall()[0][0]\n",
    "print(f'Python工程师 平均工资为{salary_python}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_func = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_func['职能']=['区块链工程师', '算法工程师','系统架构师', 'Java工程师',  'Python工程师']\n",
    "data_func['工资']=[salary_blockchain, salary_algorithm, salary_architect, salary_java, salary_python]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>职能</th>\n",
       "      <th>工资</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>系统架构师</td>\n",
       "      <td>23244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>区块链工程师</td>\n",
       "      <td>21119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>算法工程师</td>\n",
       "      <td>19317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Python工程师</td>\n",
       "      <td>18072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Java工程师</td>\n",
       "      <td>14227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          职能     工资\n",
       "2      系统架构师  23244\n",
       "0     区块链工程师  21119\n",
       "1      算法工程师  19317\n",
       "4  Python工程师  18072\n",
       "3    Java工程师  14227"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_func.sort_values(by='工资', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
